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It seems worthwhile to pause occasionally and take stock of the balance of trade between economics and health economics. Martin Feldstein (1974) did this fourteen years ago at the ASSA meetings, and sufficient time has lapsed to bring up the issue again. Here, however, I undertake only a fragment of the task, confining the discussion to some remarks about econometrics and health economics. If one were writing about economic theory and health economics, one might focus on the importance for health economics of both uncertainty and the physician's dual role as supplier and patient agent. In discussing econometrics and health economics, however, other features of health economics are relevant:
Toward a Formal Science of Economics provides a unifying way to look at the concept of economic science. Toward a Formal Science of Economics provides a unifying way to look at the concept of economic science. It lays a foundation for the axiomatic method, focusing on applications in economics and econometrics, and including discussions in logic, epistemology, and probability theory. Each chapter deals with a topic of fundamental importance to a rigorous science of economics while illustrating an aspect of the axiomatic method. Stigum describes an introductory course in mathematical logic, developing a symbolic language for mathematics and discussing the strengths and weaknesses of the axiomatic method. He presents the standard theory of consumer choice, illustrating different aspects of the use of the axiomatic method and evaluating economic theories of individual behavior. He takes up problems in the foundations of econometrics and choice under uncertainty and offers an introduction to nonstandard analysis that leads to discussion of exchange and probability in hyperspace. A section on epistemology completes Stigum's construction of a formal unitary methodological basis for theoretical and empirical science. The last three parts of the book apply these methodological tools to various topics in economics and econometrics including empirical analyses of the permanent income hypothesis and consumer choice among risky and nonrisky assets; discussion of determinism, uncertainty, and the utility hypothesis; and study of topics of importance to the analysis of economic time series.
An extensive synthesis is provided of the concepts, measures and techniques of Information Theory (IT). After an axiomatic description of the basic definitions of “information functions”, “entropy” or uncertainty and the maximum entropy principle, the paper demonstrates the power of IT as both an interpretive and techinically productive tool. It is argued that this power and universality is promarily due to the common need for (i) measures of distance and discrimination and, (ii) appropriate partitioning- aggregation properties. IT offers a very suggestive unification for a bewildering and arbitrary set of approaches that have evolved in different disciplines. Applications are discussed or indicated. These applications have relevance to economics, finance, industrial organization, marketing, statistical ingerence and model selection, political science and communication. A main focus of the discussion is the generative power of IT measures in statistical examinations of unknown distributions and random phe...
"Vibe coding" and "vibe analytics" have been framed as a democratization of technical capability. This paper argues that AI-assisted methodology more broadly, or what I call "vibe methodology," also democratizes the failure modes specific to each domain. When AI assists with methods whose validity depends on assumptions that cannot be verified from the output alone (a class I call "vibe inference"), the failure surface is structurally different: the output does not reliably signal invalidity, and when it does, recognizing the signal requires the expertise the workflow bypasses. I focus on "vibe econometrics," the subset of AI-assisted causal analysis where identification can be named faster than it can be audited. The claim of this paper is not that AI invents inferential failures that did not previously exist, but that it changes their incidence, observability, and persuasive force enough to create a practically distinct governance problem. This results in three failure modes: method-data mismatch, where AI bypasses expertise at execution; confidence laundering, where AI amplifies the credibility of formatted output; and invisible forking, which spans both. What is new is not the
This classic text has proven its worth in university classrooms and as a tool kit in research--selling over 40,000 copies in the United States and abroad in its first edition alone. Users have included undergraduate and graduate students of economics and business, and students and researchers in political science, sociology, and other fields where regression models and their extensions are relevant. The book has also served as a handy reference in the real world for people who need a clear and accurate explanation of techniques that are used in empirical research.Throughout the book the emphasis is on simplification whenever possible, assuming the readers know college algebra and basic calculus. Jan Kmenta explains all methods within the simplest framework, and generalizations are presented as logical extensions of simple cases. And while a relatively high degree of rigor is preserved, every conflict between rigor and clarity is resolved in favor of the latter. Apart from its clear exposition, the book's strength lies in emphasizing the basic ideas rather than just presenting formulas to learn and rules to apply.The book consists of two parts, which could be considered jointly or separately. Part one covers the basic elements of the theory of statistics and provides readers with a good understanding of the process of scientific generalization from incomplete information. Part two contains a thorough exposition of all basic econometric methods and includes some of the more recent developments in several areas.As a textbook, Elements of Econometrics is intended for upper-level undergraduate and master's degree courses and may usefully serve as a supplement for traditional Ph.D. courses in econometrics. Researchers in the social sciences will find it an invaluable reference tool.A solutions manual is also available for teachers who adopt the text for coursework.Jan Kmenta is Professor Emeritus of Economics and Statistics, University of Michigan.
Can AI effectively perform complex econometric analysis traditionally requiring human expertise? This paper evaluates AI agents' capability to master econometrics, focusing on empirical analysis performance. We develop ``MetricsAI'', an Econometrics AI Agent built on the open-source MetaGPT framework. This agent exhibits outstanding performance in: (1) planning econometric tasks strategically, (2) generating and executing code, (3) employing error-based reflection for improved robustness, and (4) allowing iterative refinement through multi-round conversations. We construct two datasets from academic coursework materials and published research papers to evaluate performance against real-world challenges. Comparative testing shows our domain-specialized AI agent significantly outperforms both benchmark large language models (LLMs) and general-purpose AI agents. This work establishes a testbed for exploring AI's impact on social science research and enables cost-effective integration of domain expertise, making advanced econometric methods accessible to users with minimal coding skills. Furthermore, our AI agent enhances research reproducibility and offers promising pedagogical applic
This paper presents a novel quantitative approach for comparative economic studies, addressing limitations in current classification methods. Conventional approaches in comparative economics often rely on ad hoc and categorical classifications, leading to subjective judgments and disregarding the continuous nature of the spectrum of economic systems. These can result in subjectivity and significant information loss, particularly for countries with systems near categorical borders. To overcome these shortcomings, the present paper proposes distance-based indices for objective categorization, considering economic foundations and using hard data. Accordingly, the paper introduces institutional similarity indices--Capitalism Similarity Index (CapSI), Communism Similarity Index (ComSI), and Socialism Similarity Index (SocSI)-which reflect countries' positions along the economic system continuum. These indices adhere to mathematical rigor and are grounded in the mathematical fields of real analysis, metric spaces, and distance functions. By classifying 135 countries and creating GIS maps, the practical applicability of the proposed approach is demonstrated. Results show a high explanator
The goal of this paper is to investigate the importance of providing visual "big pictures" in the teaching of economics. The plurality and variety of concepts, variables, diagrams, and models involved in economics can be a source of confusion for many economics students. However, reviewing the existing literature on the importance of providing visual "big pictures" in the process of learning suggests that furnishing students with a visual "big picture" that illustrates the ways through which those numerous, diverse concepts are connected to each other could be an effective solution to clear up the mentioned mental chaos. As a practical example, this paper introduces a "big picture" that can be used as a good resource in intermediate macroeconomics classes. This figure presents twenty-seven commonly-discussed macroeconomic diagrams in the intermediate macroeconomics course, and gives little detail on some of these diagrams, aiming at helping students to get the whole picture at once on a single piece of paper. This macroeconomics big picture mostly focuses on the routes through which common diagrams in macroeconomics are connected to each other, and finally introduces the general ma
This study investigates the relationship between the market volatility of the iShares Asia 50 ETF (AIA) and economic and market sentiment indicators from the United States, China, and globally during periods of economic uncertainty. Specifically, it examines the association between AIA volatility and key indicators such as the US Economic Uncertainty Index (ECU), the US Economic Policy Uncertainty Index (EPU), China's Economic Policy Uncertainty Index (EPUCH), the Global Economic Policy Uncertainty Index (GEPU), and the Chicago Board Options Exchange's Volatility Index (VIX), spanning the years 2007 to 2023. Employing methodologies such as the two-covariate GARCH-MIDAS model, regime-switching Markov Chain (MSR), and quantile regressions (QR), the study explores the regime-dependent dynamics between AIA volatility and economic/market sentiment, taking into account investors' sensitivity to market uncertainties across different regimes. The findings reveal that the relationship between realized volatility and sentiment varies significantly between high- and low-volatility regimes, reflecting differences in investors' responses to market uncertainties under these conditions. Additiona
This paper establishes the theoretical and practical foundations for using Large Language Models (LLMs) as measurement instruments for latent economic variables -- specifically variables that describe the cognitive content of occupational tasks at a level of granularity not achievable with existing survey instruments. I formalize four conditions under which LLM-generated scores constitute valid instruments: semantic exogeneity, construct relevance, monotonicity, and model invariance. I then apply this framework to the Augmented Human Capital Index (AHC_o), constructed from 18,796 O*NET task statements scored by Claude Haiku 4.5, and validated against six existing AI exposure indices. The index shows strong convergent validity (r = 0.85 with Eloundou GPT-gamma, r = 0.79 with Felten AIOE) and discriminant validity. Principal component analysis confirms that AI-related occupational measures span two distinct dimensions -- augmentation and substitution. Inter-rater reliability across two LLM models (n = 3,666 paired scores) yields Pearson r = 0.76 and Krippendorff's alpha = 0.71. Prompt sensitivity analysis across four alternative framings shows that task-level rankings are robust. Obv
With the reversal of Roe v. Wade in 2022, many U.S. employers announced they would reimburse employees for abortion-related travel expenses. This action complements increasingly common employer policies subsidizing employee access to assisted reproductive technologies such as in-vitro fertilization and egg freezing. This article reflects on why employers offer these benefits and whether they enhance or undermine reproductive justice. From the employer's perspective, abortion and assisted reproductive technologies help women to plan childbearing around the demands of their jobs. Both are associated with delayed childbirth and reduced fertility, which lower the costs of motherhood to employers. However, firm subsidization of these services does not further reproductive justice because it reifies structures which incentivize women to delay childbirth and reduce fertility, and it reinforces economic and reproductive inequalities. We conclude by questioning whether reproductive justice is possible without transforming the economy so that it prioritizes care over profits.
Rapid increases in food supplies have reduced global hunger, while rising burdens of diet-related disease have made poor diet quality the leading cause of death and disability around the world. Today's "double burden" of undernourishment in utero and early childhood then undesired weight gain and obesity later in life is accompanied by a third less visible burden of micronutrient imbalances. The triple burden of undernutrition, obesity, and unbalanced micronutrients that underlies many diet-related diseases such as diabetes, hypertension and other cardiometabolic disorders often coexist in the same person, household and community. All kinds of deprivation are closely linked to food insecurity and poverty, but income growth does not always improve diet quality in part because consumers cannot directly or immediately observe the health consequences of their food options, especially for newly introduced or reformulated items. Even after direct experience and epidemiological evidence reveals relative risks of dietary patterns and nutritional exposures, many consumers may not consume a healthy diet because food choice is driven by other factors. This chapter reviews the evidence on diet
The idea of duality has proved to be a powerful device in modern work on the economics of consumer behaviour. The authors have used duality to provide an integrated and accessible treatment of this subject. The book focuses on applications of the theory to welfare economics and econometric analysis. The book begins with four chapters that provide a self-contained presentation of the basic theory and its use in applied econometrics. These chapters also include elementary extensions of the theory to labour supply, durable goods, the consumption function, and rationing. The rest of the book is divided into three parts. In the first of these the authors discuss restrictions on choice and aggregation problems. The next part consists of chapters on consumer index numbers; household characteristics, demand, and household welfare comparisons; and social welfare and inequality. The last part extends the coverage of consumer behaviour to include the quality of goods and household production theory, labour supply and human capital theory, the consumption function and intertemporal choice, the demand for durable goods, and choice under uncertainty
We analyse 'stop-and-go' containment policies that produce infection cycles as periods of tight lockdowns are followed by periods of falling infection rates. The subsequent relaxation of containment measures allows cases to increase again until another lockdown is imposed and the cycle repeats. The policies followed by several European countries during the Covid-19 pandemic seem to fit this pattern. We show that 'stop-and-go' should lead to lower medical costs than keeping infections at the midpoint between the highs and lows produced by 'stop-and-go'. Increasing the upper and reducing the lower limits of a stop-and-go policy by the same amount would lower the average medical load. But increasing the upper and lowering the lower limit while keeping the geometric average constant would have the opposite effect. We also show that with economic costs proportional to containment, any path that brings infections back to the original level (technically a closed cycle) has the same overall economic cost.
This paper investigates Large Language Models (LLMs) ability to assess the economic soundness and theoretical consistency of empirical findings in spatial econometrics. We created original and deliberately altered "counterfactual" summaries from 28 published papers (2005-2024), which were evaluated by a diverse set of LLMs. The LLMs provided qualitative assessments and structured binary classifications on variable choice, coefficient plausibility, and publication suitability. The results indicate that while LLMs can expertly assess the coherence of variable choices (with top models like GPT-4o achieving an overall F1 score of 0.87), their performance varies significantly when evaluating deeper aspects such as coefficient plausibility and overall publication suitability. The results further revealed that the choice of LLM, the specific characteristics of the paper and the interaction between these two factors significantly influence the accuracy of the assessment, particularly for nuanced judgments. These findings highlight LLMs' current strengths in assisting with initial, more surface-level checks and their limitations in performing comprehensive, deep economic reasoning, suggesti
Recent studies in psychology and neuroscience offer systematic evidence that fictional works exert a surprisingly strong influence on readers and have the power to shape their opinions and worldviews. Building on these findings, we study what we term Potterian economics, the economic ideas, insights, and structure, found in Harry Potter books, to assess how the books might affect economic literacy. A conservative estimate suggests that more than 7.3 percent of the world population has read the Harry Potter books, and millions more have seen their movie adaptations. These extraordinary figures underscore the importance of the messages the books convey. We explore the Potterian economic model and compare it to professional economic models to assess the consistency of the Potterian economic principles with the existing economic models. We find that some of the principles of Potterian economics are consistent with economists models. Many other principles, however, are distorted and contain numerous inaccuracies, contradicting professional economists views and insights. We conclude that Potterian economics can teach us about the formation and dissemination of folk economics, the intuiti
This paper investigates the economic feasibility of replacing human labor with robotics and automation in Qatar's manufacturing and service sectors. By analyzing labor costs, productivity gains, and implementation expenses, the study assesses the potential financial impact and return on investment of robotic integration. Results indicate the sectors where automation is economically viable and identify challenges related to workforce adaptation, policy, and infrastructure. These insights provide guidance for policymakers and industry stakeholders considering automation strategies in Qatar.
A fundamental challenge for modern economics is to understand what happens when actors in an economy are replaced with algorithms. Like rationality has enabled understanding of outcomes of classical economic actors, no-regret can enable the understanding of outcomes of algorithmic actors. This review article covers the classical computer science literature on no-regret algorithms to provide a foundation for an overview of the latest economics research on no-regret algorithms, focusing on the emerging topics of manipulation, statistical inference, and algorithmic collusion.
This study analyses the impacts of economic complexity on environmental performance in BRICS-T countries. Annual data for the period 1999-2021, Durbin-Hausman cointegration test and Augmented Mean Group (AMG) estimator are used in the analysis. The robustness of the Panel AMG results is tested with CCEMG and CS-ARDL methods. The results indicate that economic complexity has a positive impact on environmental performance. An increase of 1% in the economic complexity index increases environmental performance in BRICS-T countries between 0.020% and 1.243%. However, economic growth, energy intensity and population density were found to have a negative impact on environmental performance. Renewable energy use, in contrast, contributes positively to environmental performance.
Business growth is a goal of great importance for its both private and social benefits. Many firms view business growth as an imperative for their survival, stability, and long-term success. Business growth can be socially beneficial, too, as it enables businesses to expand into new territories where they can stimulate economic growth and development, creates more jobs, increase living standards, and better serve their communities by giving back more through Corporate Social Responsibility initiatives. Business growth must be planned reasonably and optimally so that it can effectively achieve its critical ambitions in business practice. The current common practices for planning the supply side of business growth are usually ad-hoc and lack well-established mathematical and economic foundations. The present paper argues that business growth planning can be pursued more structurally, reliably, and meaningfully within the framework of Growth Accounting (GA), which was first introduced by Economics Nobel Laureate Robert Solow to study economic growth. It is shown that, although GA was initially put forth as a procedure to explain "economic growth" ex-post, it can similarly be used to p